Vision-based Recognition of Human Behaviour for Intelligent Environments

Vision-based Recognition of Human
Behaviour for Intelligent Environments
Alexandros Andre Chaaraoui
Departamento de Tecnolog´ Inform´tica y Computaci´n
ıa
a
o
Universidad de Alicante
alexandros@ua.es

Supervisor: Dr. Francisco Fl´rez-Revuelta
o

January 20, 2014

Alexandros Andre Chaaraoui (UA)

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Overview

1

Introduction

2

Research framework

3

Contributions

4

Results

5

Concluding remarks


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Introduction

Introduction


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Introduction

Motivation

Introduction (I)

Motivation
Demographic ageing - Ambient-assisted living (AAL)
Intelligent environments
Human behaviour analysis


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Introduction

Objectives

Introduction (II)

In this thesis, our goal is to support the development of AAL services
for smart homes with advances in human behaviour analysis.
Main objectives
1

Establish the research framework

2

Propose a method for the recognition of human behaviour

3

Satisfy speciﬁc demands of AAL services

4

Reach robustness for diﬀerent scenarios


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Research framework

Research framework


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Research framework

Related work

Research framework
Proposed taxonomy

Figure 1: Human Behaviour Analysis levels — Classiﬁcation proposed
in Chaaraoui et al. (2012).


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Research framework

Proposal

Conclusions of the analysis

Motion
Motion, pose and gaze estimation is the most resolved level.
Action
Action recognition is currently receiving the greatest interest both from
research and industry.
Activity-Behaviour
Activity and long-term behaviour recognition is performed directly
based on low-level sensor data, instead of using action recognition.


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Research framework

Proposal

Camera 1

Long-term
Analysis

Setup
(activities,
inhabitants,
objects…),
Profiles and
Log

Camera 2

...

Camera N

Motion
Detection

Motion
Detection

...

Motion
Detection

Human
Behaviour
Analysis

Human
Behaviour
Analysis

...

Human
Behaviour
Analysis

Multi-view Human Behaviour Analysis

Environmental
Sensor Information

Reasoning
System

Privacy

Event
Alarm

Actuators

Caregiver

Figure 2: Architecture of the intelligent monitoring system to promote
independent living at home and support AAL services.

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Contributions

Contributions


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Contributions

Outline

Contributions

Proposals have been made at diﬀerent processing stages:
1

Pose representation

2

Fusion of multiple views

3

Action classiﬁcation

4

Evolutionary-based optimisation

5

Continuous recognition


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Contributions

Pose representation

Pose representation

Images

Silhouettes

Pose
Representations

Figure 3: Outline of the pose representation process. Based on the recorded
video frames, foreground segmentations are obtained. Holistic features can
then be extracted relying on the shape of the human silhouettes.

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Contributions

Pose representation

Radial Summary (I)

Figure 4: Overview of the feature extraction process: 1) All the contour points are
assigned to the corresponding radial bin; 2) for each bin, a summary representation
is obtained (example with 18 bins).

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Contributions

Pose representation

Radial Summary (II)

Figure 5: Graphical explanation of the statistical range frange of a sample
radial bin.

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Contributions


Multiple views of the same ﬁeld of view provide
Additional characteristic data
Advantages with respect to occlusions
Advantages with respect to unfavourable viewing angles
(ambiguous actions)
However, diﬃculties have been observed
The recognition does not necessarily improve
Performance issues (temporal and spatial)
Burdensome, highly-restricted systems (3D pose estimation,
calibrated and synchronised camera networks, ...)


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Contributions


Weighted feature fusion scheme
Weights are learnt for each view and action:

Figure 6: Overview of the feature fusion process of the multi-view pose
representation. This example shows five different views of a specific pose taken
from the walk action class from the IXMAS dataset (Weinland et al., 2006).

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Contributions


Key Poses

K Key
Poses

...

...

...

Bag of Key
Poses

K Key
Poses

Sequences of
Key Poses

Figure 7: Outline of the learning stage. Using the pose representations, key
poses are obtained for each action. In this way, a bag-of-key-poses model is
learnt. The temporal relation between key poses is modelled using sequences
of key poses.

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Contributions


Bag of key poses

Action1

K Key Poses

Action2

K Key Poses

...

...

...

...

ActionA

K Key Poses

Bag of Key Poses

Figure 8: Learning scheme of the bag-of-key-poses model. For each action
class, K key poses are obtained separately and then joined together.

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Contributions


Recognition
Sequences
of Key Poses

Sequence
Matching

DTW

Action
Recognition

Figure 9: Outline of the recognition stage. The unknown sequence of key
poses is obtained and compared to the known sequences. Through sequence
matching, the action of the video sequence can be recognised.

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Contributions


Genetic feature subset selection
By means of a genetic algorithm for binary feature selection, the
interesting body parts can be selected, and redundant or noisy body
parts can be ignored.

Figure 10: Example of a result provided by genetic feature selection (dismissed
radial bins are shaded in grey).

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Contributions



Coevolutionary optimisation
Simultaneous selection of training instances, features and
parameter values
Coevolution enables to split the problem in subproblems of
optimisation with a common goal (Wiegand, 2004)
Cooperative coevolution allows to consider intrinsic dependencies
among optimisation goals (Derrac et al., 2012)


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Contributions



Coevolutionary optimisation
Adaptive learning
Evolving bag of key poses
Supports incremental and adaptive learning of new data
Applies selection of training instances, features and parameter
values


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Contributions


In AAL, human action recognition has to be applied to continuous
video streams.
This requires:
To detect meaningful human actions online
And to recognise the appropriate action in real-time
We propose:
To learn action zones, i.e. the most discriminative parts of
action performances
The usage of a sliding and growing window technique for
recognition


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Contributions


Action zones

Figure 11: Sample silhouettes of a waving sequence of the DAI RGBD
dataset. The action zone that should be extracted is highlighted.


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Contributions


Action zones

Figure 12: Evidence values H(t) of each action class and the detected action
zones are shown for a scratch head sequence of the IXMAS dataset.

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Results

Results


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Results

Evaluation methodology

Results

Experimentation has been performed on:
Single- and multi-view datasets (up to ﬁve views)
Manually- and automatically-extracted silhouettes (including
depth-based segmentation)
Using the following cross validations:
Leave one sequence out (LOSO)
Leave one actor out (LOAO)
Leave one view out (LOVO)


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Results

Benchmarks

Weizmann dataset
Table 1: Comparison of recognition rates and speeds obtained on the
Weizmann dataset (Gorelick et al., 2007) with other state-of-the-art
approaches.
Approach
˙
Ikizler and Duygulu (2007)
Tran and Sorokin (2008)
Fathi and Mori (2008)

Actions
9
10
10

Test
LOSO
LOSO
LOSO

Rate
100%
100%
100%

fps
N/A
N/A
N/A

Hern´ndez et al. (2011)
a
Cheema et al. (2011)
Sadek et al. (2012)

10
9
10

LOAO
LOSO
LOAO

90.3%
91.6%
97.8%

98
56
18

Our approach
Our approach

10
10

LOSO
LOAO

93.5%
97.8%

188
188

Optimised approach

10

LOAO

100%

210


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Results

Benchmarks

MuHAVi-14 dataset

MuHAVi-14 dataset (Singh et al., 2010) with other state-of-the-art
approaches.
Approach

LOSO

LOAO

LOVO

fps

Singh et al. (2010)
Eweiwi et al. (2011)

82.4%
91.9%

61.8%
77.9%

42.6%
55.8%

N/A
N/A


86.0%

73.5%

50.0%

56

98.5%

94.1%

59.6%

99

100%

100%

-

-

Our approach
Optimised approach


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Results

Benchmarks

MuHAVi-8 dataset

MuHAVi-8 dataset (Singh et al., 2010) with other state-of-the-art approaches.
M´todo
e

LOSO

LOAO

LOVO

fps

Singh et al. (2010)
Mart´
ınez-Contreras et al. (2009)
Eweiwi et al. (2011)

97.8%
98.4%
98.5%

76.4%
85.3%

50.0%
38.2%

N/A
N/A
N/A


95.6%

83.1%

57.4%

56

Our approach

100%

100%

82.4%

98


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Results

Benchmarks

IXMAS dataset
Table 4: Comparison with other multi-view human action recognition
approaches of the state of the art. The rates obtained in the LOAO cross
validation performed on the IXMAS dataset are shown.
Approach

Actions

Actors

Views

Rate

fps

Yan et al. (2008)
Wu et al. (2011)
Cilla et al. (2012)
Weinland et al. (2006)
Cilla et al. (2013)
Holte et al. (2012)

11
12
11
11
11
13

12
12
12
10
10
12

4
4
5
5
5
5

78%
89.4%
91.3%
93.3%
94.0%
100%

N/A
N/A
N/A
N/A
N/A
N/A

Cherla et al. (2008)
Weinland et al. (2010)

13
11

N/A
10

4
5

80.1%
83.5%

20
∼500

Our approach

11

12

5

91.4%

207


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Results

Benchmarks

RGBD datasets
DAI RGBD dataset
Table 5: Cross validation results obtained on our multi-view depth dataset.
Approach

LOSO

LOAO

fps

Our approach

94.4%

100%

80

DHA dataset
Table 6: LOSO cross validation results obtained on the DHA dataset (Lin
et al., 2012) (10 Weizmann actions).
Approach

LOSO

Lin et al. (2012)
Our approach

fps

90.8%

N/A

95.2%

99

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Results

Benchmarks


Table 7: Obtained results applying CHAR and segment analysis evaluation
(LOAO). Results are detailed using the segmented sequences or the proposed
action zones.
Dataset

Approach

F1 -measure

IXMAS
IXMAS

Segmented sequences
Action zones

0.504
0.705

Weizmann
Weizmann

Segmented sequences
Action zones

0.693
0.928


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Concluding remarks

Concluding remarks


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Concluding remarks

Discussion

Discussion
Silhouettes
2D silhouettes can be diﬃcult to obtain and they are view dependant
Privacy
Privacy concerns of indoor monitoring
Intelligent monitoring system with privacy protection
The method only relies on the boundary of the silhouette
Validation of the proposed method
The classiﬁcation method based on the bag of key poses has also been
validated for gaming and NUI (Chaaraoui et al., 2014, 2013;
Climent-P´rez et al., 2013)
e


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Concluding remarks

Conclusions

Conclusions

1

Proposal of a 2D template-based non-parametric method for
human action recognition & optimisations and extensions

2

Specific demands of AAL services have been satisfied: relaxed
camera setup requirements, adaptive learning, continuous
recognition and real-time execution

3

The HAR method based on a bag-of-key-poses model handles
single- and multi-view recognition proficiently.

4

State-of-the-art recognition rates have been achieved,
outperforming the best known rates in several benchmarks.


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Concluding remarks

Future work

Future work
Future directions of this work:
Pose representation: Other local and global features
Key poses: Generation algorithms
Bag-of-key-poses model: Applications
Distance metrics: Key poses and sequences of key poses
Evaluation and deployment

However, two main future lines stand out:
Recognition of complex activities based on action sequences
and multi-modal data
Feature fusion techniques, e.g. for recognition of subtle movements
or gestures


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Other information and details



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Research projects and grants

Research projects and grants
Intelligent system for follow-up and promotion of personal
autonomy: Computer vision system for the monitoring of
activities of daily living at home – Sistema de visiń para la
o
monitorizaciń de la actividad diaria en el hogar. Spanish Ministry of Science and
o
Innovation and Valencian Ministry of Education, Culture and Sport (TALISMAN+,
Technical University of Madrid, University of Deusto, University of Castile-La
Mancha and University of Alicante)

PhD. Research Fellowship – Programa VALi+d para investigadores en
formaciń. Valencian Ministry of Education, Culture and Sport (ACIF/2011/160)
o

Research Collaboration Stay – Digital Imaging Research Centre, Faculty
of Science, Engineering and Computing, Kingston University. Kingston upon
Thames, UK. (Funded by VALi+d, BEFPI/2013/015)

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Other activities

Other activities

Teaching collaboration – Information Technology. First year core subject of
Degree in Sound and Image in Telecommunication Engineering

Reviewer of international journals – Neurocomputing (Elsevier),
Pervasive Computing (IEEE), EURASIP Journal on Image and Video Processing
(Springer), Expert Systems With Applications (Elsevier)

Conference session chair – IEEE/RSJ Intelligent Robots and Systems
(IROS 2012), Genetic and Evolutionary Computation Conference (GECCO 2013)


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Intl. review

International review

This thesis has been approved
by the following reviewers for the
International PhD Honourable Mention

Dr. Jean-Christophe Nebel
(Kingston University, UK)

Dr. Jes´s Mart´
u
ınez del Rinc´n
o
(Queen’s University of Belfast, UK)


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Publications

Publications
Journals
I Chaaraoui, A.A., Climent-P´rez, P., Fl´rez-Revuelta, F., 2012. A Review on
e
o
Vision Techniques Applied to Human Behaviour Analysis for Ambient-Assisted
Living. Expert Systems with Applications. Citations: 10

II Chaaraoui, A.A., Climent-P´rez, P., Fl´rez-Revuelta, F., 2013.
e
o
Silhouette-based Human Action Recognition Using Sequences of Key Poses.
Pattern Recognition Letters. Citations: 6

III Chaaraoui, A.A., Fl´rez-Revuelta, F., 2013. Optimizing Human Action
o
Recognition Based on a Cooperative Coevolutionary Algorithm. Engineering
Applications of Artiﬁcial Intelligence.


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Publications

Publications
IV Chaaraoui, A.A., Padilla-L´pez, J.R., Climent-P´rez, P., Fl´rez-Revuelta, F.,
o
e
o
2014. Evolutionary Joint Selection to Improve Human Action Recognition
with RGB-D Devices. Expert Systems with Applications.

V Chaaraoui, A.A., Fl´rez-Revuelta, F., 2014. A Low-Dimensional Radial
o
Silhouette-based Feature for Fast Human Action Recognition Fusing Multiple
Views. Information Fusion. Under review

VI Chaaraoui, A.A., Fl´rez-Revuelta, F., 2014. Adaptive Human Action
o
Recognition With an Evolving Bag of Key Poses. IEEE Transactions on
Autonomous Mental Development. Under review


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Publications

Publications
Conferences and workshops
I Intl. Symposium on Ambient Intelligence (ISAmI 2012)
II 3rd Intl. Workshop on Human Behavior Understanding (HBU 2012),
IEEE/RSJ Intl. Conference on Intelligent Robots and Systems (IROS 2012)

III 11th Mexican Intl. Conference on Artiﬁcial Intelligence (MICAI 2012)
IV Genetic and Evolutionary Computation Conference (GECCO 2013)
V 5th Intl. Work-conference on Ambient Assisted Living (IWAAL 2013)
VI 3rd Workshop on Consumer Depth Cameras for Computer Vision
(CDC4CV13), IEEE Intl. Conference on Computer Vision (ICCV 2013)


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Publications

“We can only see a short distance ahead, but we can see
plenty there that needs to be done.” (Turing, 1950)

Vision-based Recognition of Human Behaviour
for Intelligent Environments
PhD Thesis


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References

References
Chaaraoui, A. A., P. Climent-P´rez, and F. Fl´rez-Revuelta (2012). A review on vision
e
o
techniques applied to human behaviour analysis for ambient-assisted living. Expert
Systems with Applications 39 (12), 10873 – 10888.
Chaaraoui, A. A., J. R. Padilla-L´pez, P. Climent-P´rez, and F. Fl´rez-Revuelta (2014).
o
e
o
Evolutionary joint selection to improve human action recognition with RGB-D devices.
Expert Systems with Applications 41 (3), 786 – 794. Methods and Applications of
Artiﬁcial and Computational Intelligence.
Chaaraoui, A. A., J. R. Padilla-L´pez, and F. Fl´rez-Revuelta (2013). Fusion of skeletal
o
o
and silhouette-based features for human action recognition with RGB-D devices. In
IEEE 14th International Conference on Computer Vision Workshops, 2013. ICCV
Workshops 2013. To be presented in 3rd Workshop on Consumer Depth Cameras for
Computer Vision (CDC4CV13).
Cheema, S., A. Eweiwi, C. Thurau, and C. Bauckhage (2011). Action recognition by
learning discriminative key poses. In IEEE 13th International Conference on Computer
Vision Workshops, 2011. ICCV Workshops 2011, pp. 1302 –1309.
Cherla, S., K. Kulkarni, A. Kale, and V. Ramasubramanian (2008). Towards fast,
view-invariant human action recognition. In IEEE Conference on Computer Vision and
Pattern Recognition Workshops, 2008. CVPRW 2008, pp. 1 – 8.


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References

References
Cilla, R., M. A. Patricio, A. Berlanga, and J. M. Molina (2012). A probabilistic,
discriminative and distributed system for the recognition of human actions from multiple
views. Neurocomputing 75 (1), 78 – 87. Brazilian Symposium on Neural Networks (SBRN
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Cilla, R., M. A. Patricio, A. Berlanga, and J. M. Molina (2013). Human action recognition
with sparse classification and multiple-view learning. Expert Systems. DOI
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Climent-P´rez, P., A. A. Chaaraoui, J. R. Padilla-L´pez, and F. Fl´rez-Revuelta (2013).
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human action recognition. In IEEE 13th International Conference on Computer Vision
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References

References
Fathi, A. and G. Mori (2008). Action recognition by learning mid-level motion features. In
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Holte, M., B. Chakraborty, J. Gonzalez, and T. Moeslund (2012). A local 3-D motion
descriptor for multi-view human action recognition from 4-D spatio-temporal interest
points. IEEE Journal of Selected Topics in Signal Processing 6 (5), 553 – 565.
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References

References
Lin, Y.-C., M.-C. Hu, W.-H. Cheng, Y.-H. Hsieh, and H.-M. Chen (2012). Human action
recognition and retrieval using sole depth information. In Proceedings of the 20th ACM
international conference on Multimedia, MM ’12, New York, NY, USA, pp. 1053 – 1056.
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(2009). Recognizing human actions using silhouette-based HMM. In IEEE Int.
Conference on Advanced Video and Signal Based Surveillance, 2009. AVSS 2009, pp. 43
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human activity recognition. International Journal of Intelligence Science 2 (1), 9 – 15.
Singh, S., S. Velastin, and H. Ragheb (2010). MuHAVi: A multicamera human action video
dataset for the evaluation of action recognition methods. In IEEE Int. Conference on
Advanced Video and Signal Based Surveillance, 2010. AVSS 2010, pp. 48 – 55. IEEE.
Tran, D. and A. Sorokin (2008). Human activity recognition with metric learning. In
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References

References
¨
Weinland, D., M. Ozuysal, and P. Fua (2010). Making action recognition robust to
occlusions and viewpoint changes. In K. Daniilidis, P. Maragos, and N. Paragios (Eds.),
European Conference on Computer Vision. ECCV 2010, Volume 6313 of Lecture Notes
in Computer Science, pp. 635 – 648. Springer Berlin / Heidelberg.
Weinland, D., R. Ronfard, and E. Boyer (2006). Free viewpoint action recognition using
motion history volumes. Computer Vision and Image Understanding 104 (2-3), 249 –
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Wiegand, R. P. (2004). An analysis of cooperative coevolutionary algorithms. Ph. D. thesis,
George Mason University, Fairfax, VA, USA.
Wu, X., D. Xu, L. Duan, and J. Luo (2011). Action recognition using context and
appearance distribution features. In IEEE Conference on Computer Vision and Pattern
Recognition, 2011. CVPR 2011, pp. 489 – 496.
Yan, P., S. Khan, and M. Shah (2008). Learning 4D action feature models for arbitrary
view action recognition. In IEEE Conference on Computer Vision and Pattern
Recognition, 2008. CVPR 2008, pp. 1 – 7.


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Copyright

Copyright

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Copyright

Author’s contact details

alexandros@ua.es
www.alexandrosandre.com
Departamento de Tecnolog´ Inform´tica y Computaci´n
ıa
a
o
Universidad de Alicante
Carretera San Vicente del Raspeig s/n
E-03690 San Vicente del Raspeig (Alicante) - Spain


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Vision-based Recognition of Human Behaviour for Intelligent Environments

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